Principal Machine Learning Engineer - VoIP Infrastructure

ServiceNow•Santa Clara, CA

12h•Hybrid

About The Position

PLATO (Platform Engineering and AI Technology Organization) at ServiceNow is a customer-focused innovative group building intelligent software using a variety of technology stacks to enable end-to-end, industry-leading work experiences for our customers. We are a group of people deeply invested in the success of our customers that happen to have expertise and knowledge in advanced technologies and software engineering best practices. We are data driven, structured, committed and we enjoy what we are doing. We prioritize robustness, performance and user experience over the technology stack and tools. You will play a major part in building AI and Machine Learning (ML) solutions that transform the user experience and workflow efficiency of enterprise services. Traditional analytical tools tend to require a technically knowledgeable user to produce even simple results. We are taking a completely fresh approach with the expectation that any user, regardless of technical knowledge, can use the AI/ML solutions we develop to operate the services in the enterprise setting in a thoughtful and scalable manner. We are just getting started with our early-adopter customers and we need your help in building and making available an amazing range of solutions to our 5k+ enterprise customers around the world.

Requirements

Extensive experience building VoIP systems using SIP/RTP protocols;
Practical knowledge of Kamailio, RTPEngine, FreeSWITCH, SBCs, and PSTN systems (or similar);
Working knowledge of PSTN infrastructure and telecom protocols;
Experience integrating applications on top of LLMs (using existing models, not building them);
Experience in prompt engineering and developing LLM based features
10+ years of development experience with Python, GoLang, Java or similar languages.
10+ years of experience operating highly available distributed workloads on Kubernetes following a DevOps approach.
Working experience building distributed systems with cloud-native software;
Experience with software-defined networking, infrastructure as code and configuration management;
Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
Experience in using AI productivity tools such as Cursor, Windsurf, etc

Nice To Haves

Experience with DevOps tooling (e.g. Helm / Ansible / Kubernetes / Prometheus /Splunk/ GitLab CI) is considered an asset
Experience building software for compliance and security in regulated environments is considered an asset
10+ years of experience with infrastructure and platform operations, deployments, SRE, and DevOps with a continued focus on improving Platform health is considered an asset

Responsibilities

Lead the design and implementation of VoIP and CCaaS integrations, connecting ServiceNow's AI platform with enterprise contact center providers.
Design and architect VoIP infrastructure, telephony platforms, and observability features that power AI-driven voice workloads
Collaborate with engineering, Product, and infrastructure teams to ensure our voice and AI platforms perform efficiently, scale reliably, and integrate seamlessly across SIP/RTP, Kamailio, RTPEngine, and related telecom systems.
Contribute to the continuous improvement of the SRE practice by turning operational telephony and AI workload use cases into requirements for software tooling.
Lead the execution of deployment and support activities for VoIP systems and AI/ML developers operating in production voice environments.
Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.).
Work with product owners to understand detailed requirements and own your code from design, implementation, test automation, and delivery — spanning both telephony infrastructure and LLM integration layers.
Integrate AI capabilities — including LLM-powered features and conversational workflows — into real-time voice pipelines.
Be a mentor for colleagues and help promote knowledge-sharing across telecom and AI engineering disciplines.